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A Unified Cropland Layer at 250 m for Global Agriculture Monitoring, , , , , , , , , and 6 other author(s). Data, 1 (1): 1-13 (March 2016)Global Cropland Yield Monitoring with Gaussian Processes., , , , , , and . IGARSS, page 180-183. IEEE, (2021)Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps., , , and . Remote. Sens., 7 (6): 7959-7986 (2015)Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series., , , , , , and . Remote. Sens., 10 (2): 159 (2018)Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m., , and . Remote. Sens., 8 (3): 232 (2016)Operational Monitoring of the Desert Locust Habitat with Earth Observation: An Assessment., , , and . ISPRS Int. J. Geo Inf., 4 (4): 2379-2400 (2015)Local adjustments of image spatial resolution to optimize large-area mapping in the era of big data., , and . Int. J. Appl. Earth Obs. Geoinformation, (2018)Accuracy Assessment of the First Eu-Wide Crop Type Map with Lucas Data., , , and . IGARSS, page 1990-1993. IEEE, (2021)Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model., , , , and . Remote. Sens., 7 (12): 16204-16225 (2015)ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data., , , и . CoRR, (2019)